Title: Computational Advances in High-Throughput Biological Data Analysis
Speaker: Michael A. Langston, University of Tennessee

We will discuss the utility of innovative graph algorithms in the analysis 
of high-throughput biological data. Using gene co-expression network analysis 
as a central theme, we will describe the use of novel tools for handling 
noisy data, and the role of model organisms in successful applications to 
human health.

We will also discuss the utility of powerful computational platforms. Algorithms 
designed for shared memory machines can be difficult to translate effectively 
to distributed memory models. Moreover, problems are frequently of an enumerative 
flavor, and thus output bound. The situation is confounded by FPGAs, multi-core 
processors, GPUs, green computing and other so-called disruptive technologies.

Efficient combinatorial search remains a core concern. As time permits, we will 
also touch on the quest for biomarkers and machine learning.